eEF2K knockdown synergizes STS to inhibit the angiogenesis of lung adenocarcinoma via TG2/IL-6/VEGFR2 pathway
Bibliographic record
Abstract
Abstract Background: Lung adenocarcinoma (LUAD) is the most popular type of lung cancer. Eukaryotic elongation factor 2 kinase (eEF2K) is considered to be a carcinogen, and Sulfotanshinone IIA sodium (STS) has been proved to have anticancer effect. However, their role in LUAD and its underlying mechanism remains unclear.Objective: To figuring out the roles of eEF2K and STS in angiogenesis of LUAD and their underlying mechanism.Methods: The mRNA levels of genes were detected by qRT-PCR. The levels of proteins were measured by Western blot. The secretion of IL-6 was assessed by ELISA. The proliferation and angiogenesis of human umbilical vein endothelial cells (HUVECs) were detected by EdU assay and tubule formation assay, respectively.Results: Our results illustrated that eEF2K knockdown-LUAD cells synergize STS inhibited the proliferation and angiogenesis of HUVECs, eEF2K knockdown synergized STS inhibited the secretion of interleukin 6 (IL-6) in LUAD cells, and eEF2K knockdown-LUAD cells synergized STS to inhibit the proliferation and angiogenesis of HUVECs, which were dependent on IL-6 secretion and Transglutaminase 2 (TG2). Moreover, eEF2K knockdown-LUAD cells synergized STS inhibited IL-6/STAT3/VEGFR2 pathway depended on TG2. Conclusion: Our research ultimately illustrated that eEF2K knockdown synergized STS to inhibit the angiogenesis of LUAD via TG2/IL-6/VEGFR2 pathway.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".